About|About the Index|Indexing Criteria|Open Access Policy|Submit Journal|FAQs|Contact

PHYSICS-INFORMED HYBRID MODELING FOR PREDICTIVE CONDITION MONITORING OF A GEAR-DRIVEN COTTON GIN MACHINE

item.page.link.simple
dc.contributor.authorYunusov Odiljon Makhmudjon ogli
dc.contributor.authorSaydaliyev Abdulbori Abdulvohid ogli
dc.contributor.authorKhoshimov Adxam Akhmadjon ogli
dc.contributor.authorSharifbayev Rakhinjon Nasir ogli
dc.contributor.authorKhurshud Madaliyev Bahrom ogli
dc.contributor.authorKnyazev Mikhail Aleksandrovich
dc.date.accessioned2026-02-26T20:30:26Z
dc.date.issued2026-02-26
dc.description.abstractRotating machinery reliability remains a cornerstone of industrial productivity, particularly in cotton processing plants where gin machines operate under highly variable mechanical loads. Unexpected drivetrain failures may cause substantial production losses and energy inefficiencies. This study proposes a physics-informed hybrid modeling framework for predictive condition monitoring of a gearbox-driven cotton gin machine powered by a 75 kW induction motor.
dc.formatapplication/pdf
dc.identifier.urihttps://usajournals.org/index.php/2/article/view/2012
dc.identifier.urihttps://asianeducationindex.com/handle/123456789/117100
dc.language.isoeng
dc.publisherModern American Journals
dc.relationhttps://usajournals.org/index.php/2/article/view/2012/2094
dc.rightshttps://creativecommons.org/licenses/by/4.0
dc.sourceModern American Journal of Engineering, Technology, and Innovation; Vol. 2 No. 2 (2026); 8-19
dc.source3067-7939
dc.subjectPredictive maintenance, hybrid modeling, vibration diagnostics, gearbox dynamics, Kalman filter, condition monitoring, cotton gin machine, industrial analytics.
dc.titlePHYSICS-INFORMED HYBRID MODELING FOR PREDICTIVE CONDITION MONITORING OF A GEAR-DRIVEN COTTON GIN MACHINE
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typePeer-reviewed Article

item.page.files

item.page.filesection.original.bundle

pagination.showing.labelpagination.showing.detail
thumbnail.default.alt
item.page.filesection.name
ogli_2026_physics-informed_hybrid_modeling_for_pre.pdf
item.page.filesection.size
645.03 KB
item.page.filesection.format
Adobe Portable Document Format
item.page.filesection.download

item.page.collections

Modern American Journal of Engineering, Technology, and Innovation